计算机工程与应用 ›› 2007, Vol. 43 ›› Issue (5): 24-27.
• 学术探讨 • 上一篇 下一篇
粒子群优化算法分析及研究进展
朱丽莉，杨志鹏，袁华
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Analysis and Development of Particle Swarm Optimization
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摘要： 粒子群优化算法是一类基于群体智能的启发式全局优化技术，群体中的每一个微粒代表待解决问题的一个候选解，算法通过粒子间信息素的交互作用发现复杂搜索空间中的最优区域。本文介绍了粒子群优化算法的基本原理，并通过建立记忆表，详尽描述了粒子群优化算法中个体极优和全局极优的搜寻求解过程。同时，文章给出了多种改进形式以及研究现状，并提出了未来可能的研究方向。
关键词: 启发式, 记忆表, 粒子群优化算法, 群体智能
Abstract: Particle swarm optimization algorithm is a heuristic global optimization technique based on swarm intelligence. Each particle of the swarm represents one candidate solution of the problem being optimized. The algorithm finds optimal regions of complex problem spaces through the pheromone interaction of particles. This paper reviews the basic theory, and describes the seeking procedure of personal best and global best in PSO through establishing memory table. At the same time, this paper also presents some kinds of improved versions of PSO and research situation, and then future research issues are also given.
Key words: heuristic, 记忆表, particle swarm optimization, swarm intelligence
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